De-Risking Delivery with Flow and Data

Delivery pressure is mounting and most teams are stuck reacting instead of improving. If you’re a senior engineering leader responsible for predictable delivery, this hands-on workshop will give you a practical, systems-level approach to spot the friction, manage the flow, and engineer more confidence in your delivery timelines.

In just half a day, you’ll apply a structured set of tools from Flow Engineering and the Kanban Guide to visualize how work actually moves through your org, uncover the root causes of delivery risk, and walk away with clear, actionable steps to improve outcomes now.

Led by three industry experts—Colleen Johnson, Daniel Vacanti, and Steve Pereira—this is a one-time opportunity to learn from the leaders shaping how high-performing teams deliver faster, without chaos or burnout. Only while they’re in town for Craft Conference.

What we’ll cover:

Part 1: Make the Flow Visible
We can’t define what better looks like without alignment on where we are starting from. Then visualize and measure how work is actually flowing through your current system.

1. Outcome Mapping: Define a meaningful target outcome

2. Current-State Value Stream Mapping (VSM): Make bottlenecks and blockers visible

3. Flow Metrics: Use simple data to identify hidden risks and constraints

A shared understanding of where your delivery process breaks down and what’s slowing you down most.

Part 2: Design a Smarter System

Modern software delivery systems are fragile, overloaded, over-optimized, and increasingly dependent on cross-team coordination. This brittle systems are at increased risk as AI make it easy to continue to increase the volume of changes in progress.

1. Future-State VSM: Design a more resilient value stream

2. Dependency Mapping: Expose high-risk handoffs and collaboration gaps

3. Managing Active Work in the Age of AI: Reduce noise and maintain clarity

 A set of system-level interventions to stabilize flow and regain control over delivery timelines.

Part 3: Engineering for Confidence

Forecasting only works when the system supports it. In this session, you’ll learn how to design for predictability not just model it.

1. Use insights from Parts 1 & 2 to surface where forecasting confidence breaks down

2. Understand how high WIP, aging work, and dependencies degrade delivery reliability

3. Create conditions for trustworthy probabilistic forecasts without adding process overhead

 A practical path to more confident delivery commitments without relying on estimates or rigid time boxes.

Space is limited. This is your chance to work directly with the people defining the future of flow, metrics, and delivery. If your team needs to move faster and hit their dates, you don’t want to miss this.